Because the objective of FedCG is to improve the performance of each client's personalized local network validating on local test data, we further compare the accuracy gains between FedCG and 5 FL baselines over the Local. Figure REF  shows the results. In IID scenarios, all FL methods outperform Local on all clients by large margins, as shown in Figures REF  and REF . Particularly, FedCG performs best on FMNIST(4) while FedProx performs best on CIFAR10(8). In non-IID scenarios, while no FL method can beat Local on every client across all 3 non-IID datasets, FedCG achieves the best result such that it outperforms Local on 12 out of 13 clients, as shown in Figure REF , REF  and REF . FedAvg, FedProx and FedDF are not excel in non-IID scenarios as they are in IID scenarios because the average-based global model may be far from client's local optima [1]}. Besides, the distillation dataset leveraged by FedDF is from a different domain than those of clients, which may have negative impacts on the performance of aggregated global model.
